tree-ring-memory
Manages an AI agent's memory efficiently and locally. Allows the agent to recall, audit, and forget information without saving full transcripts.
What it does
This tool helps the AI remember and organize important project information. It allows the AI to store lessons, decisions, and warnings in an organized way, without needing to keep entire conversations.
When to use it
- ✓Before resuming a project to recall past decisions.
- ✓Before making major changes to how the project works.
- ✓When you ask the AI to remember, review, or delete information.
Example of what to ask your AI
“AI, remind me of the important decisions about this project's architecture.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI checks for existing local instructions to organize memory. If none are found, it can install a tool to help store and retrieve information structurally, like lessons or warnings.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill tree-ring-memoryOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/tree-ring-memory/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Tree Ring Memory
Overview
Tree Ring Memory is a framework-agnostic, local-first memory lifecycle layer for AI agents. Use this skill when an agent should recall, preserve, audit, or forget durable project memory without treating raw conversation transcripts as memory.
The public runtime is a Rust CLI/TUI with local SQLite/FTS storage, scoped recall, evidence records, audit, deterministic consolidation, maintenance, DOX/Revolve source adapters, framework discovery, redaction, and explicit forgetting.
When to Use This Skill
- Use before resuming a project where prior decisions, warnings, preferences, or failed approaches may matter.
- Use before changing architecture, storage, security, privacy, release, or agent-memory behavior.
- Use when the user asks to remember, recall, audit, redact, forget, or consolidate agent memory.
- Use after tests, reviews, incidents, or production behavior validate a lesson future agents should preserve.
- Use when a project contains
.tree-ring/SKILL.md,.tree-ring/CLI.md, or other Tree Ring bridge files.
How It Works
Step 1: Discover Local Guidance
Check whether the current project already has Tree Ring guidance:
test -f .tree-ring/SKILL.md && sed -n '1,220p' .tree-ring/SKILL.md
test -f .tree-ring/CLI.md && sed -n '1,220p' .tree-ring/CLI.md
Treat project-local .tree-ring files as more authoritative than generic
examples in this skill. If the CLI is installed, inspect the current command
surface before assuming flags:
tree-ring --help
tree-ring recall --help
tree-ring remember --help
tree-ring evidence --help
tree-ring audit --help
tree-ring forget --help
If Tree Ring is not installed, resolve the actual project root and explain the
exact project-local download before obtaining explicit approval to fetch it.
Download the official, version-pinned v0.15.0/install.sh to a temporary file,
verify its SHA-256 is
ef0d5eb8f09cbe2e4c3abe80ee9a98a56759c89ad4ddd103d6c68314cd653ade,
inspect it, then run it. The pinned source is
https://raw.githubusercontent.com/TerminallyLazy/Tree-Ring-Memory/v0.15.0/install.sh.
cd <project-root>
sh <verified-installer-path> --project --init --release v0.15.0 --no-animation
Do not pipe a network response directly to a shell. After verification and
inspection, show the exact installer command and obtain explicit approval again
before executing it. For an installed global CLI, initialize from the project root with
tree-ring --root .tree-ring init; for a project-local CLI, use
.tree-ring/bin/tree-ring --root .tree-ring init.
Check for a newer release without changing files using
tree-ring update --check. Run tree-ring update only with user authorization,
then rerun init in each project root to refresh managed guidance while
preserving custom content.
Step 2: Recall Before Risky Work
Use narrow, project-scoped recall first:
tree-ring recall "release behavior" --project example-service
tree-ring recall "sqlite migration" --project example-service
tree-ring recall "user preference"
Use recalled memory as context, not authority. Verify it against current source files, tests, docs, issues, pull requests, logs, and runtime state before making changes.
Step 3: Write Only Durable Memory
Write concise memory only when it is likely to help future agents:
tree-ring remember "Run project-scoped recall before release changes." --event-type lesson --scope project
Prefer specific event types when supported locally:
decisionlessonwarningcorrectionuser_preferencetool_resultsummaryhypothesis
Store the durable lesson, decision, warning, or follow-up. Do not store the full conversation.
Step 4: Record Evidence for Evaluated Outcomes
Use evidence records for test runs, incidents, reviewed changes, or other evaluated outcomes:
tree-ring evidence \
"Installer smoke test passed in an isolated HOME." \
--outcome observed \
--evidence-ref "ci/install-smoke/2026-07-08"
Outcome guidance:
promoted: durable truth backed by strong evidencerejected: failed or rolled-back approach worth keeping visibledeferred: unresolved idea or future optionobserved: normal evaluated result
Do not promote weak, stale, or unreviewed claims to durable truth.
Step 5: Use Source Adapters Carefully
When a repo has structured source records, run dry runs first:
…
FAQ
What does 'local-first' mean?
It means the information is primarily stored on your computer, not on remote servers.
Can I delete information the AI has stored?
Yes, the tool allows you to delete or forget specific information when it's no longer useful or is incorrect.
Will the AI store all my conversations?
No, the goal is to store only important and useful project information, like lessons learned or decisions made, not the entire conversation.
Details
Original description by the author
Use Tree Ring Memory for local-first AI-agent memory lifecycle work: recall, evidence, audit, forgetting, and consolidation without transcript dumping.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4404
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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